Abstract — A nonlinear adaptive time series predictor has been developed using a new type of piecewise linear (PWL) network for its underlying model structure. The PWL Network is a D-FANN (Dynamical Functional Artificial Neural Network) the activation functions of which are piecewise linear. The new realization is presented with the associated training algorithm. Properties and characteristics are discussed. This network has been successfully used to model and predict an important class of highly dynamic and non-stationary signals, namely speech signals
金沢大学大学院自然科学研究科情報システムTime series prediction is very important technology in a wide variety of fields....
Nonlinear techniques for signal processing and recognition have the promise of achieving systems whi...
A computationally efficient artificial neural network (ANN) for the purpose of dynamic nonlinear sys...
A nonlinear adaptive time series predictor has been developed using a new type of piecewise linear (...
Abstract: In this paper we describe a neural network for the nonlinear adaptive prediction of non-st...
The analysis of an observed univariate time series is often undertaken in order to get a prediction ...
The problem of predicting nonlinear and nonstationary signals is complex since the physical law that...
An attempt has been made to establish a nonlinear dynamic discrete-time neuron model, the so called ...
金沢大学大学院自然科学研究科情報システムA nonlinear time series predictor was proposed, in which a nonlinear sub-predict...
Abstract—The paper is devoted to time series prediction using linear, perceptron and Elman neural ne...
A novel linearised Recursive Least Squares (LRLS) learning algorithm is presented for an adaptive no...
Time series prediction is a very important technology in a wide variety of field. The actual time se...
This paper reports about a comparative study on several linear and nonlinear feedforward and recurre...
Time-series prediction and forecasting is much used in engineering, science and economics. Neural ne...
For prediction of nonlinear and nonstationary signals, as well as in nonlinear system identification...
金沢大学大学院自然科学研究科情報システムTime series prediction is very important technology in a wide variety of fields....
Nonlinear techniques for signal processing and recognition have the promise of achieving systems whi...
A computationally efficient artificial neural network (ANN) for the purpose of dynamic nonlinear sys...
A nonlinear adaptive time series predictor has been developed using a new type of piecewise linear (...
Abstract: In this paper we describe a neural network for the nonlinear adaptive prediction of non-st...
The analysis of an observed univariate time series is often undertaken in order to get a prediction ...
The problem of predicting nonlinear and nonstationary signals is complex since the physical law that...
An attempt has been made to establish a nonlinear dynamic discrete-time neuron model, the so called ...
金沢大学大学院自然科学研究科情報システムA nonlinear time series predictor was proposed, in which a nonlinear sub-predict...
Abstract—The paper is devoted to time series prediction using linear, perceptron and Elman neural ne...
A novel linearised Recursive Least Squares (LRLS) learning algorithm is presented for an adaptive no...
Time series prediction is a very important technology in a wide variety of field. The actual time se...
This paper reports about a comparative study on several linear and nonlinear feedforward and recurre...
Time-series prediction and forecasting is much used in engineering, science and economics. Neural ne...
For prediction of nonlinear and nonstationary signals, as well as in nonlinear system identification...
金沢大学大学院自然科学研究科情報システムTime series prediction is very important technology in a wide variety of fields....
Nonlinear techniques for signal processing and recognition have the promise of achieving systems whi...
A computationally efficient artificial neural network (ANN) for the purpose of dynamic nonlinear sys...
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